Galaxy Plots on Sims
Katie Lotterhos
1/16/2017
Evaluating Galaxy plots with known simulation data
Recently I have been exploring the idea of visualizing SNP effect sizes to multiple environmental variables. Here, I am extending the results from the I am using the Lotterhos and Whitlock (2015) simulations to explore this concept in a known scenario. Basically, we want to know if the two distinct groups of behavior we observed in the dataset could be generated by chance if there is only one environmental variable driving the adapatation.
I have chosen three replicate datasets from the LW2015 simulations, that were simulated range expansions in a 1 refugia model, with 90 populations sampled and 20 individuals per population. This demography is similar to the range expansion we expect in lodgepole pine, which is the species and dataset I am applying the concept to (Adaptree data). The total sample size is also similar - in the paper I think we have ~600 diploids (1200 chromosomes), whereas here we have 1800 haploid chromosomes.
In LW2015, we simulated adaptation of loci to a single environmental variable (E_main).
Here, I am going to overlay the Adaptree environmental variables. With this data, I am going to essentially repeat the steps that I took for the Adaptree data:
calculate uncorrected Spearman’s rho association between allele frequency (\(f\)) and environment (\(E_i\)).
choose outliers based on uncorrected Spearman’s rho as those that have P-values less than the Bonferroni correction. Hereafter these will be called the “candidate set”.
use Galaxy plots to visualize the candidate set in 2 dimensional space.
I will conduct this exercise for datasets with just neutral loci (9900) and for datasets with neutral loci and loci simulated under varying strengths of selection (9900 and 100).
setwd("Users/katie/Desktop/CurrResearch/1-AdaptreeData/201509_PEGA/simulations/")Load data
sample_locs <- read.table("data/LW2015samplingdesigns/SchemeRandom1.txt")
dim(sample_locs)## [1] 90 7head(sample_locs)## PopID X_Pops Y_Pops R90 R60 R30 NSRangeTrans_30
## 1 1 314 345 TRUE TRUE TRUE 0
## 2 2 307 330 TRUE FALSE FALSE 0
## 3 3 195 27 TRUE FALSE FALSE 2
## 4 4 34 357 TRUE FALSE FALSE 0
## 5 5 5 84 TRUE FALSE FALSE 1
## 6 6 328 96 TRUE TRUE TRUE 0## These were created on a 360 x 360 grid
range(sample_locs$X_Pops)## [1] 5 351 range(sample_locs$Y_Pops)## [1] 4 358### Adaptree environments
env_AT <- read.csv("../data/MAT06-BLUEs&Climate-pine.csv")
head(env_AT)## ProvincialSeedlotsID ExperimentalClimate HtFinalS01_mm HeightFinalS02_mm
## 1 AB_GSIMS_1956 MAT06 35.65049 81.00325
## 2 AB_GSIMS_2186 MAT06 42.29694 107.99599
## 3 AB_GSIMS_2193 MAT06 NA NA
## 4 AB_GSIMS_2206 MAT06 36.05810 118.99837
## 5 AB_GSIMS_2212 MAT06 32.38957 87.99843
## 6 AB_GSIMS_2235 MAT06 39.36665 116.50214
## FinalStemDiamS02_mm MaxGthRate_mm_Day FinalShootWeightS02_g
## 1 2.191825 1.519222 1.078899
## 2 3.223240 2.646411 3.806046
## 3 NA NA NA
## 4 3.158580 2.302429 3.639656
## 5 2.385529 2.274559 1.549045
## 6 2.890474 2.315339 2.162529
## FinalRootWeightS02_g TotalWeight RootShoot_Ratio BudSet
## 1 0.3477735 1.430072 0.3021461 168.5562
## 2 1.2560344 5.057673 0.3342296 144.9283
## 3 NA NA NA NA
## 4 1.8873532 5.524971 0.5244722 144.9671
## 5 0.9172172 2.464662 0.4875097 151.4742
## 6 0.7251025 2.889891 0.3036566 161.0375
## BudBreakS02_day LinearGthS02_Days Gth5pctS02_Day Gth95pctS02_Day
## 1 45.92458 5.393948 132.2515 174.6045
## 2 47.75024 4.130711 139.4520 175.7595
## 3 49.92475 NA NA NA
## 4 45.40084 5.456177 126.4605 179.1811
## 5 49.92462 3.962001 135.6939 169.3393
## 6 46.01196 5.000022 129.4992 176.8384
## Gth5.95pctS02_Days ColdInjuryFallS02_Mean ColdInjuryMidWinterS02_Mean
## 1 42.40250 76.92681 72.96522
## 2 37.04660 43.09167 35.57998
## 3 NA NA NA
## 4 54.09819 51.11874 38.71432
## 5 33.34892 66.96808 65.57299
## 6 47.25170 51.62439 46.62356
## ColdInjurySpringS02_Mean Latitude Longitude Elevation MAT MWMT MCMT
## 1 69.22961 49.67 -110.25 1160 3.4 16.9 -10.4
## 2 67.20300 54.72 -115.42 1160 1.8 13.8 -9.9
## 3 NA 53.90 -119.07 1220 2.3 13.5 -8.5
## 4 67.60140 54.65 -118.83 1021 2.0 14.1 -11.2
## 5 70.10960 54.42 -119.00 1189 2.1 13.7 -9.6
## 6 36.07398 55.63 -113.45 914 -0.8 13.9 -19.0
## TD log.MAP. log.MSP. AHM SHM DD0 DD5 NFFD bFFP eFFP FFP PAS EMT
## 1 27.3 6.068426 5.549076 31.0 65.9 1179 1405 151 149 248 99 128 -42.5
## 2 23.8 6.504288 6.156979 17.6 29.3 1305 1032 151 146 258 112 163 -41.2
## 3 22.0 6.368187 5.886104 21.1 37.5 1126 982 146 158 252 94 174 -42.0
## 4 25.3 6.408529 5.958425 19.8 36.5 1296 1074 153 148 255 106 174 -43.6
## 5 23.3 6.532334 6.059123 17.6 32.0 1206 1001 153 151 258 107 202 -42.5
## 6 32.9 6.222576 5.826000 18.3 40.9 2055 930 136 160 244 83 149 -46.3
## EXT Eref CMD
## 1 37.4 660 350
## 2 29.6 462 35
## 3 31.0 497 96
## 4 31.1 490 64
## 5 30.1 463 35
## 6 31.2 470 94which(names(env_AT)=="Latitude")## [1] 20env_AT <- env_AT[complete.cases(env_AT),20:ncol(env_AT)]
plot(env_AT$Longitude, env_AT$Latitude)
### Transform X and Y to match (0 to 360) while preserving
X1 <- range(env_AT$Longitude, na.rm=TRUE)
Y_range <- range(env_AT$Latitude, na.rm=TRUE)
X1 <- env_AT$Longitude - min(env_AT$Longitude, na.rm=TRUE)
Y1 <- env_AT$Latitude - min(env_AT$Latitude, na.rm=TRUE)
plot(X1, Y1)
# multiply both sides by a factor to get the landscape to fit on 360 x 360
X2 <- X1*360/max(X1, na.rm=TRUE)
Y2 <- Y1*360/max(Y1, na.rm=TRUE)
plot(X2, Y2)
Interpolate
For each variable, also compare (i) distributions and (ii) autocorrelation between real environments and resampled environment from the 1R model.
env_AT$x <- X2
env_AT$y <- Y2
env_AT2 <- env_AT
coordinates(env_AT) = ~x + y
grd <- expand.grid(x = 1:360, y = 1:360) # expand points to grid
coordinates(grd) <- ~x + y
gridded(grd) <- TRUE
par(mfrow=c(1,1))
plot(grd, cex = 1.5, col = "grey")
points(env_AT, pch = 1, col = "red", cex = 1)
points(sample_locs$X_Pops, sample_locs$Y_Pops, col="blue", pch="*")
names(env_AT)## [1] "Latitude" "Longitude" "Elevation" "MAT" "MWMT"
## [6] "MCMT" "TD" "log.MAP." "log.MSP." "AHM"
## [11] "SHM" "DD0" "DD5" "NFFD" "bFFP"
## [16] "eFFP" "FFP" "PAS" "EMT" "EXT"
## [21] "Eref" "CMD"for (j in 3:22){
# interpolate!
thisvarname <- names(env_AT2)[j]
print(c(j, thisvarname))
thisdat <- env_AT[,j]
names(thisdat) = "e"
head(thisdat)
idw <- idw(formula = e ~ 1, locations = thisdat, newdata = grd)
idw.output = as.data.frame(idw) # output is defined as a data table
names(idw.output)[1:3] <- c("long", "lat", "e.pred") # give names to the modelled variables
head(idw.output)
ggplot() + geom_tile(data = idw.output, aes(x = long, y = lat, fill = e.pred)) + geom_point(data = env_AT2, aes(x = X2, y = Y2), shape = 21,
colour = "red") + geom_point(data = sample_locs, aes(x = X_Pops, y = Y_Pops), shape = 25,
colour = "yellow")
#head(sample_locs)
for (i in 1:nrow(sample_locs)){
wrow <- which(idw.output$long==sample_locs$X_Pops[i] & idw.output$lat==sample_locs$Y_Pops[i])
sample_locs$e[i] <- idw.output$e.pred[wrow]
}
head(sample_locs)
names(sample_locs)[ncol(sample_locs)] <- thisvarname
par(mfrow=c(2,1), mar=c(2,4,2,0))
b<-hist(env_AT2[,j], main=paste("Real", thisvarname))
hist(sample_locs[,ncol(sample_locs)], breaks=b$breaks, main="Interpolated")
ncf.cor.real <- correlog(env_AT2$x, env_AT2$y, env_AT2[,j], increment=2, resamp=500, quiet = TRUE)
ncf.cor.sample <- correlog(sample_locs$X_Pops, sample_locs$Y_Pops, sample_locs[,ncol(sample_locs)], increment=2, resamp=500, quiet = TRUE)
par(mfrow=c(2,1), mar=c(2,4,2,0))
plot(ncf.cor.real$correlation, ylim= c(-2,2), main=paste("True",thisvarname), type="l")
plot(ncf.cor.sample$correlation, ylim= c(-2,2), main="Interpolated", type="l")
}## [1] "3" "Elevation"
## [inverse distance weighted interpolation]
## [1] "4" "MAT"
## [inverse distance weighted interpolation]
## [1] "5" "MWMT"
## [inverse distance weighted interpolation]
## [1] "6" "MCMT"
## [inverse distance weighted interpolation]
## [1] "7" "TD"
## [inverse distance weighted interpolation]
## [1] "8" "log.MAP."
## [inverse distance weighted interpolation]
## [1] "9" "log.MSP."
## [inverse distance weighted interpolation]
## [1] "10" "AHM"
## [inverse distance weighted interpolation]
## [1] "11" "SHM"
## [inverse distance weighted interpolation]
## [1] "12" "DD0"
## [inverse distance weighted interpolation]
## [1] "13" "DD5"
## [inverse distance weighted interpolation]
## [1] "14" "NFFD"
## [inverse distance weighted interpolation]
## [1] "15" "bFFP"
## [inverse distance weighted interpolation]
## [1] "16" "eFFP"
## [inverse distance weighted interpolation]
## [1] "17" "FFP"
## [inverse distance weighted interpolation]
## [1] "18" "PAS"
## [inverse distance weighted interpolation]
## [1] "19" "EMT"
## [inverse distance weighted interpolation]
## [1] "20" "EXT"
## [inverse distance weighted interpolation]
## [1] "21" "Eref"
## [inverse distance weighted interpolation]
## [1] "22" "CMD"
## [inverse distance weighted interpolation]
Compare correlation matrices of raw and interpolated environments
Compare correlation structure between real environments and resampled environment from the 1R model.
head(env_AT2)## Latitude Longitude Elevation MAT MWMT MCMT TD log.MAP. log.MSP. AHM
## 1 49.67 -110.25 1160 3.4 16.9 -10.4 27.3 6.068426 5.549076 31.0
## 2 54.72 -115.42 1160 1.8 13.8 -9.9 23.8 6.504288 6.156979 17.6
## 4 54.65 -118.83 1021 2.0 14.1 -11.2 25.3 6.408529 5.958425 19.8
## 5 54.42 -119.00 1189 2.1 13.7 -9.6 23.3 6.532334 6.059123 17.6
## 6 55.63 -113.45 914 -0.8 13.9 -19.0 32.9 6.222576 5.826000 18.3
## 7 59.70 -117.98 731 -3.9 14.0 -24.4 38.4 6.003887 5.480639 15.0
## SHM DD0 DD5 NFFD bFFP eFFP FFP PAS EMT EXT Eref CMD x
## 1 65.9 1179 1405 151 149 248 99 128 -42.5 37.4 660 350 358.6867
## 2 29.3 1305 1032 151 146 258 112 163 -41.2 29.6 462 35 273.8167
## 4 36.5 1296 1074 153 148 255 106 174 -43.6 31.1 490 64 217.8386
## 5 32.0 1206 1001 153 151 258 107 202 -42.5 30.1 463 35 215.0479
## 6 40.9 2055 930 136 160 244 83 149 -46.3 31.2 470 94 306.1560
## 7 58.3 3031 843 123 156 239 82 168 -49.3 32.2 384 144 231.7921
## y
## 1 20.82645
## 2 187.76860
## 4 185.45455
## 5 177.85124
## 6 217.85124
## 7 352.39669(creal <- cor(env_AT2[,1:22]))## Latitude Longitude Elevation MAT MWMT
## Latitude 1.00000000 -0.34593875 -0.53016376 -0.54886123 -0.04849022
## Longitude -0.34593875 1.00000000 0.42872283 -0.14759206 0.20578507
## Elevation -0.53016376 0.42872283 1.00000000 -0.33268858 -0.63674973
## MAT -0.54886123 -0.14759206 -0.33268858 1.00000000 0.57909805
## MWMT -0.04849022 0.20578507 -0.63674973 0.57909805 1.00000000
## MCMT -0.69766323 -0.24399906 0.06882824 0.84960653 0.10215145
## TD 0.65461824 0.31365271 -0.30834498 -0.59964860 0.28140359
## log.MAP. -0.29487631 -0.09505132 0.17046127 0.22994038 -0.21385952
## log.MSP. 0.15519310 0.34130874 0.19976655 -0.22916110 -0.22278969
## AHM -0.07333708 0.02035374 -0.27871371 0.31325250 0.47826670
## SHM -0.19142689 -0.18696160 -0.35397959 0.41282772 0.53146188
## DD0 0.68749271 0.21986874 0.03472285 -0.92036724 -0.23606663
## DD5 -0.13195970 0.07445573 -0.67528833 0.75228521 0.95854093
## NFFD -0.11255267 -0.18990280 -0.62917655 0.77878308 0.67477746
## bFFP -0.15060059 -0.07118125 0.67799164 -0.50064911 -0.76795501
## eFFP -0.14330071 -0.22576324 -0.51709661 0.74725518 0.54292792
## FFP 0.01593969 -0.06588542 -0.63705718 0.64794854 0.70338667
## PAS -0.32617106 -0.09301837 0.42135593 -0.07007206 -0.40359400
## EMT -0.49983394 -0.33700331 -0.23772478 0.83158935 0.30670129
## EXT -0.33139430 0.14801221 -0.43406237 0.62661226 0.85041382
## Eref -0.53112833 0.16972381 -0.29877992 0.78499959 0.75647150
## CMD -0.31274782 -0.18044142 -0.30331352 0.50028497 0.51627501
## MCMT TD log.MAP. log.MSP. AHM
## Latitude -0.69766323 0.65461824 -0.29487631 0.155193099 -0.073337080
## Longitude -0.24399906 0.31365271 -0.09505132 0.341308737 0.020353741
## Elevation 0.06882824 -0.30834498 0.17046127 0.199766552 -0.278713715
## MAT 0.84960653 -0.59964860 0.22994038 -0.229161096 0.313252500
## MWMT 0.10215145 0.28140359 -0.21385952 -0.222789686 0.478266702
## MCMT 1.00000000 -0.92574395 0.45266071 -0.136402077 0.046828661
## TD -0.92574395 1.00000000 -0.51742435 0.047428830 0.136156987
## log.MAP. 0.45266071 -0.51742435 1.00000000 0.510182726 -0.824746879
## log.MSP. -0.13640208 0.04742883 0.51018273 1.000000000 -0.639300317
## AHM 0.04682866 0.13615699 -0.82474688 -0.639300317 1.000000000
## SHM 0.17925937 0.02877209 -0.50797851 -0.925876764 0.738338590
## DD0 -0.97351273 0.84955191 -0.36052506 0.186371072 -0.167812089
## DD5 0.31353975 0.06175519 -0.09869558 -0.216102097 0.462967338
## NFFD 0.52946082 -0.25406727 0.27236537 -0.066537066 0.111971441
## bFFP -0.13822530 -0.15887049 -0.07891956 -0.100986047 -0.128993433
## eFFP 0.58574828 -0.35836993 0.38458259 0.004739958 -0.011164211
## FFP 0.36188239 -0.08140803 0.23065962 0.060719830 0.069131842
## PAS 0.22982253 -0.37482895 0.77123395 0.178860381 -0.699455233
## EMT 0.84101276 -0.69464061 0.44866516 -0.263356589 0.008946374
## EXT 0.23525655 0.09591923 -0.26177716 -0.479399400 0.594765776
## Eref 0.47491366 -0.17089848 -0.15600667 -0.395836574 0.580334457
## CMD 0.28473357 -0.07912667 -0.45788108 -0.897456436 0.747462538
## SHM DD0 DD5 NFFD bFFP
## Latitude -0.19142689 0.68749271 -0.13195970 -0.11255267 -0.15060059
## Longitude -0.18696160 0.21986874 0.07445573 -0.18990280 -0.07118125
## Elevation -0.35397959 0.03472285 -0.67528833 -0.62917655 0.67799164
## MAT 0.41282772 -0.92036724 0.75228521 0.77878308 -0.50064911
## MWMT 0.53146188 -0.23606663 0.95854093 0.67477746 -0.76795501
## MCMT 0.17925937 -0.97351273 0.31353975 0.52946082 -0.13822530
## TD 0.02877209 0.84955191 0.06175519 -0.25406727 -0.15887049
## log.MAP. -0.50797851 -0.36052506 -0.09869558 0.27236537 -0.07891956
## log.MSP. -0.92587676 0.18637107 -0.21610210 -0.06653707 -0.10098605
## AHM 0.73833859 -0.16781209 0.46296734 0.11197144 -0.12899343
## SHM 1.00000000 -0.26474058 0.51748641 0.29038164 -0.18155844
## DD0 -0.26474058 1.00000000 -0.43891355 -0.56229672 0.19164850
## DD5 0.51748641 -0.43891355 1.00000000 0.81088430 -0.81168803
## NFFD 0.29038164 -0.56229672 0.81088430 1.00000000 -0.86079275
## bFFP -0.18155844 0.19164850 -0.81168803 -0.86079275 1.00000000
## eFFP 0.19169258 -0.59163639 0.69065279 0.96024719 -0.79005499
## FFP 0.19673995 -0.39537130 0.79959927 0.95726079 -0.95514574
## PAS -0.28438626 -0.14125809 -0.38813131 -0.17160899 0.33707626
## EMT 0.33603370 -0.82214267 0.49797544 0.77575626 -0.41317377
## EXT 0.69989022 -0.37418189 0.83579686 0.48520037 -0.44513086
## Eref 0.61131248 -0.61197794 0.80369907 0.48719341 -0.36473543
## CMD 0.95514023 -0.38364911 0.52539226 0.25447843 -0.10186767
## eFFP FFP PAS EMT EXT
## Latitude -0.143300713 0.01593969 -0.32617106 -0.499833941 -0.33139430
## Longitude -0.225763237 -0.06588542 -0.09301837 -0.337003311 0.14801221
## Elevation -0.517096611 -0.63705718 0.42135593 -0.237724780 -0.43406237
## MAT 0.747255184 0.64794854 -0.07007206 0.831589349 0.62661226
## MWMT 0.542927924 0.70338667 -0.40359400 0.306701285 0.85041382
## MCMT 0.585748285 0.36188239 0.22982253 0.841012760 0.23525655
## TD -0.358369926 -0.08140803 -0.37482895 -0.694640609 0.09591923
## log.MAP. 0.384582588 0.23065962 0.77123395 0.448665165 -0.26177716
## log.MSP. 0.004739958 0.06071983 0.17886038 -0.263356589 -0.47939940
## AHM -0.011164211 0.06913184 -0.69945523 0.008946374 0.59476578
## SHM 0.191692577 0.19673995 -0.28438626 0.336033703 0.69989022
## DD0 -0.591636385 -0.39537130 -0.14125809 -0.822142670 -0.37418189
## DD5 0.690652789 0.79959927 -0.38813131 0.497975436 0.83579686
## NFFD 0.960247187 0.95726079 -0.17160899 0.775756255 0.48520037
## bFFP -0.790054985 -0.95514574 0.33707626 -0.413173766 -0.44513086
## eFFP 1.000000000 0.93562187 -0.05868766 0.793084423 0.32767693
## FFP 0.935621874 1.00000000 -0.22224297 0.619756924 0.41452280
## PAS -0.058687657 -0.22224297 1.00000000 0.178639013 -0.27313623
## EMT 0.793084423 0.61975692 0.17863901 1.000000000 0.38425540
## EXT 0.327676930 0.41452280 -0.27313623 0.384255398 1.00000000
## Eref 0.364042121 0.38586534 -0.25324651 0.475779573 0.90781093
## CMD 0.143692889 0.12806288 -0.23538095 0.376720621 0.76621296
## Eref CMD
## Latitude -0.5311283 -0.31274782
## Longitude 0.1697238 -0.18044142
## Elevation -0.2987799 -0.30331352
## MAT 0.7849996 0.50028497
## MWMT 0.7564715 0.51627501
## MCMT 0.4749137 0.28473357
## TD -0.1708985 -0.07912667
## log.MAP. -0.1560067 -0.45788108
## log.MSP. -0.3958366 -0.89745644
## AHM 0.5803345 0.74746254
## SHM 0.6113125 0.95514023
## DD0 -0.6119779 -0.38364911
## DD5 0.8036991 0.52539226
## NFFD 0.4871934 0.25447843
## bFFP -0.3647354 -0.10186767
## eFFP 0.3640421 0.14369289
## FFP 0.3858653 0.12806288
## PAS -0.2532465 -0.23538095
## EMT 0.4757796 0.37672062
## EXT 0.9078109 0.76621296
## Eref 1.0000000 0.71947132
## CMD 0.7194713 1.00000000head(sample_locs)## PopID X_Pops Y_Pops R90 R60 R30 NSRangeTrans_30 Elevation
## 1 1 314 345 TRUE TRUE TRUE 0 980.5921
## 2 2 307 330 TRUE FALSE FALSE 0 961.8469
## 3 3 195 27 TRUE FALSE FALSE 2 1346.5580
## 4 4 34 357 TRUE FALSE FALSE 0 922.4852
## 5 5 5 84 TRUE FALSE FALSE 1 1028.5271
## 6 6 328 96 TRUE TRUE TRUE 0 1294.2153
## MAT MWMT MCMT TD log.MAP. log.MSP. AHM
## 1 0.6724280 14.14300 -14.558235 28.69819 6.329870 5.732783 19.08832
## 2 0.4016679 14.17844 -15.301201 29.47558 6.309771 5.726908 18.90590
## 3 3.1140827 13.86779 -7.351793 21.22018 6.426370 5.416452 21.74771
## 4 -0.3975880 13.62096 -15.872037 29.49228 6.282420 5.586239 17.76339
## 5 2.5903738 13.73352 -9.277635 23.01224 6.502223 5.663558 19.69240
## 6 1.9986234 13.84836 -10.706235 24.55241 6.466997 5.837874 19.35669
## SHM DD0 DD5 NFFD bFFP eFFP FFP PAS
## 1 47.25382 1684.5939 1014.651 142.3535 155.5138 248.2102 92.75406 212.6854
## 2 47.55405 1770.2248 1010.685 141.5871 155.1883 247.6836 92.59247 207.1350
## 3 63.04856 915.1264 1035.331 153.3500 157.8748 254.8701 97.01454 293.8494
## 4 51.93678 1912.6554 907.267 134.0457 163.3684 246.1616 82.78209 235.9674
## 5 49.46319 1096.5114 1035.222 152.4599 157.9691 253.4832 95.50726 264.8630
## 6 42.33687 1240.6958 1011.372 141.4806 159.8366 248.7352 88.93582 247.4103
## EMT EXT Eref CMD
## 1 -43.96263 32.25818 491.0270 142.8683
## 2 -44.23797 32.29835 485.9397 141.7057
## 3 -37.44551 32.94855 544.4767 251.6011
## 4 -46.74088 31.59091 440.1183 155.5851
## 5 -39.75605 32.27811 521.2485 178.3484
## 6 -42.78018 32.31376 527.7532 132.4428names(sample_locs)## [1] "PopID" "X_Pops" "Y_Pops"
## [4] "R90" "R60" "R30"
## [7] "NSRangeTrans_30" "Elevation" "MAT"
## [10] "MWMT" "MCMT" "TD"
## [13] "log.MAP." "log.MSP." "AHM"
## [16] "SHM" "DD0" "DD5"
## [19] "NFFD" "bFFP" "eFFP"
## [22] "FFP" "PAS" "EMT"
## [25] "EXT" "Eref" "CMD"(csim <- cor(sample_locs[,c(2:3,8:27)]))## X_Pops Y_Pops Elevation MAT MWMT
## X_Pops 1.00000000 -0.015558769 0.438960656 -0.26357597 0.28867477
## Y_Pops -0.01555877 1.000000000 -0.538772244 -0.78111063 0.08803844
## Elevation 0.43896066 -0.538772244 1.000000000 0.03573318 -0.41924233
## MAT -0.26357597 -0.781110627 0.035733180 1.00000000 0.12540371
## MWMT 0.28867477 0.088038441 -0.419242330 0.12540371 1.00000000
## MCMT -0.32891586 -0.811880431 0.175013840 0.96580628 -0.10515383
## TD 0.36879660 0.800056974 -0.245288142 -0.90970921 0.28099766
## log.MAP. -0.18091423 -0.538248476 0.103620419 0.67738915 -0.10469407
## log.MSP. 0.47065755 0.054632649 0.265865272 -0.19444674 -0.05687606
## AHM 0.06032185 -0.004452211 -0.046116930 -0.03780797 0.26104946
## SHM -0.32050533 -0.118998500 -0.268798950 0.26855088 0.30798433
## DD0 0.29956748 0.828152513 -0.190590480 -0.96983541 0.09297180
## DD5 0.03552172 -0.294022773 -0.414056200 0.62630168 0.82449644
## NFFD -0.29871263 -0.362341974 -0.435812889 0.77611911 0.38131361
## bFFP -0.08426812 -0.132805544 0.596459739 -0.21142883 -0.71056170
## eFFP -0.35062299 -0.396537007 -0.387380093 0.80716621 0.28821957
## FFP -0.16149560 -0.167926111 -0.537806368 0.58901515 0.54164866
## PAS -0.15339431 -0.465413498 0.428384654 0.33413601 -0.39376957
## EMT -0.36858006 -0.645468247 -0.102568132 0.92727980 0.06598269
## EXT 0.17222035 -0.457383620 0.008278644 0.50943316 0.67148818
## Eref 0.10608545 -0.787944368 0.215222392 0.83001907 0.40618559
## CMD -0.32581303 -0.345085704 -0.109781480 0.45814292 0.20765096
## MCMT TD log.MAP. log.MSP. AHM
## X_Pops -0.32891586 0.3687966 -0.18091423 0.47065755 0.060321849
## Y_Pops -0.81188043 0.8000570 -0.53824848 0.05463265 -0.004452211
## Elevation 0.17501384 -0.2452881 0.10362042 0.26586527 -0.046116930
## MAT 0.96580628 -0.9097092 0.67738915 -0.19444674 -0.037807973
## MWMT -0.10515383 0.2809977 -0.10469407 -0.05687606 0.261049458
## MCMT 1.00000000 -0.9839182 0.73250666 -0.16587391 -0.135337451
## TD -0.98391822 1.0000000 -0.72384645 0.15107673 0.174921575
## log.MAP. 0.73250666 -0.7238465 1.00000000 0.28699855 -0.737781189
## log.MSP. -0.16587391 0.1510767 0.28699855 1.00000000 -0.572404307
## AHM -0.13533745 0.1749216 -0.73778119 -0.57240431 1.000000000
## SHM 0.18638425 -0.1260562 -0.27277290 -0.95209667 0.643036883
## DD0 -0.99066003 0.9731124 -0.67386256 0.19210516 0.055645007
## DD5 0.42192921 -0.2590127 0.31046256 -0.11145083 0.126449458
## NFFD 0.67836288 -0.5849142 0.66414840 -0.08511383 -0.243502014
## bFFP -0.02255193 -0.1065759 -0.13933848 -0.18001228 0.054558804
## eFFP 0.73375992 -0.6550179 0.71796301 -0.07103251 -0.293390967
## FFP 0.44727504 -0.3331761 0.50115103 0.05239098 -0.204750478
## PAS 0.47549813 -0.5294172 0.67025840 0.03975171 -0.523965673
## EMT 0.91030161 -0.8660199 0.75095624 -0.27257679 -0.203190434
## EXT 0.35685715 -0.2248011 0.09526491 -0.44093561 0.381354361
## Eref 0.73634243 -0.6386866 0.39853060 -0.24319375 0.233505843
## CMD 0.39972595 -0.3501465 -0.11304602 -0.91728876 0.599817123
## SHM DD0 DD5 NFFD bFFP
## X_Pops -0.320505326 0.299567477 0.03552172 -0.29871263 -0.084268122
## Y_Pops -0.118998500 0.828152513 -0.29402277 -0.36234197 -0.132805544
## Elevation -0.268798950 -0.190590480 -0.41405620 -0.43581289 0.596459739
## MAT 0.268550879 -0.969835413 0.62630168 0.77611911 -0.211428831
## MWMT 0.307984326 0.092971800 0.82449644 0.38131361 -0.710561698
## MCMT 0.186384254 -0.990660031 0.42192921 0.67836288 -0.022551927
## TD -0.126056188 0.973112366 -0.25901268 -0.58491424 -0.106575884
## log.MAP. -0.272772897 -0.673862564 0.31046256 0.66414840 -0.139338478
## log.MSP. -0.952096668 0.192105164 -0.11145083 -0.08511383 -0.180012284
## AHM 0.643036883 0.055645007 0.12644946 -0.24350201 0.054558804
## SHM 1.000000000 -0.217584070 0.33328744 0.17894013 0.007288909
## DD0 -0.217584070 1.000000000 -0.42265606 -0.63347536 -0.006278299
## DD5 0.333287437 -0.422656062 1.00000000 0.79903419 -0.774940756
## NFFD 0.178940132 -0.633475361 0.79903419 1.00000000 -0.684155294
## bFFP 0.007288909 -0.006278299 -0.77494076 -0.68415529 1.000000000
## eFFP 0.147273367 -0.689376045 0.73727254 0.98745841 -0.613595699
## FFP 0.083077049 -0.405709567 0.84098543 0.94189321 -0.882780158
## PAS -0.080859736 -0.447768559 -0.18336670 0.08027485 0.409712860
## EMT 0.307068033 -0.882591876 0.57962225 0.86174912 -0.260425705
## EXT 0.640888072 -0.388970751 0.71907574 0.37738625 -0.233315339
## Eref 0.409043891 -0.770087322 0.68926256 0.52028586 -0.137760868
## CMD 0.954213720 -0.441087347 0.33680382 0.21990780 0.121302865
## eFFP FFP PAS EMT EXT
## X_Pops -0.35062299 -0.16149560 -0.15339431 -0.36858006 0.172220355
## Y_Pops -0.39653701 -0.16792611 -0.46541350 -0.64546825 -0.457383620
## Elevation -0.38738009 -0.53780637 0.42838465 -0.10256813 0.008278644
## MAT 0.80716621 0.58901515 0.33413601 0.92727980 0.509433156
## MWMT 0.28821957 0.54164866 -0.39376957 0.06598269 0.671488180
## MCMT 0.73375992 0.44727504 0.47549813 0.91030161 0.356857154
## TD -0.65501793 -0.33317607 -0.52941718 -0.86601989 -0.224801092
## log.MAP. 0.71796301 0.50115103 0.67025840 0.75095624 0.095264908
## log.MSP. -0.07103251 0.05239098 0.03975171 -0.27257679 -0.440935608
## AHM -0.29339097 -0.20475048 -0.52396567 -0.20319043 0.381354361
## SHM 0.14727337 0.08307705 -0.08085974 0.30706803 0.640888072
## DD0 -0.68937605 -0.40570957 -0.44776856 -0.88259188 -0.388970751
## DD5 0.73727254 0.84098543 -0.18336670 0.57962225 0.719075739
## NFFD 0.98745841 0.94189321 0.08027485 0.86174912 0.377386253
## bFFP -0.61359570 -0.88278016 0.40971286 -0.26042570 -0.233315339
## eFFP 1.00000000 0.91251633 0.14813894 0.89010557 0.315043422
## FFP 0.91251633 1.00000000 -0.12326247 0.66435533 0.310530230
## PAS 0.14813894 -0.12326247 1.00000000 0.38027013 0.043278897
## EMT 0.89010557 0.66435533 0.38027013 1.00000000 0.440803909
## EXT 0.31504342 0.31053023 0.04327890 0.44080391 1.000000000
## Eref 0.50698662 0.37431940 0.18860230 0.68393012 0.835292128
## CMD 0.20571086 0.05854206 0.08253313 0.44709389 0.694466879
## Eref CMD
## X_Pops 0.1060854 -0.32581303
## Y_Pops -0.7879444 -0.34508570
## Elevation 0.2152224 -0.10978148
## MAT 0.8300191 0.45814292
## MWMT 0.4061856 0.20765096
## MCMT 0.7363424 0.39972595
## TD -0.6386866 -0.35014646
## log.MAP. 0.3985306 -0.11304602
## log.MSP. -0.2431937 -0.91728876
## AHM 0.2335058 0.59981712
## SHM 0.4090439 0.95421372
## DD0 -0.7700873 -0.44108735
## DD5 0.6892626 0.33680382
## NFFD 0.5202859 0.21990780
## bFFP -0.1377609 0.12130286
## eFFP 0.5069866 0.20571086
## FFP 0.3743194 0.05854206
## PAS 0.1886023 0.08253313
## EMT 0.6839301 0.44709389
## EXT 0.8352921 0.69446688
## Eref 1.0000000 0.57743225
## CMD 0.5774322 1.00000000plot(creal, csim)
abline(0,1)
par(mfrow=c(2,1))
col <- two.colors(start="red", end="blue", middle="grey")
image.plot(creal, col=col)
image.plot(csim, col=col)
Write simulated environments to file
write.table(sample_locs, "data/results_AdaptreeEnviFor_R90.txt")